
from collections import defaultdict
from math import gcd

def a__       (			A_ = 1500000   ):






  '''simple docstring'''





  __magic_name__								=			defaultdict(A_   )
  __magic_name__								=			2
  while 2 * euclid_m * (euclid_m + 1) <= limit:
    for euclid_n in range((euclid_m % 2) + 1,	A_,	2   ):
      if gcd(A_,	A_   ) > 1:
        continue
      __magic_name__								=			2 * euclid_m * (euclid_m + euclid_n)
      for perimeter in range(A_,	limit + 1,	A_   ):
        frequencies[perimeter] += 1
    euclid_m += 1
  return sum(1 for frequency in frequencies.values() if frequency == 1   )


if __name__ == "__main__":
    print(F'''{solution() = }''')



from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase     :	Union[str, Any]       			=  {
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Optional[Any]       			=  [
        'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'InformerForPrediction',
        'InformerModel',
        'InformerPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_informer import (
            INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            InformerForPrediction,
            InformerModel,
            InformerPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase     :	List[str]       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

